A weighted processing method for average wind and solar resources in a research area

Abstract

The present invention provides a weighted processing method for average wind and solar resources in a study area, comprising the following steps: using a clustering algorithm to cluster the coordinates of each adjusted wind and solar project site in the study area to obtain a wind and solar project site clustering area; allocating the total installed capacity of the clustering area to grid points to obtain a wind and solar resource matrix for the study area, and then cross-multiplying and summing the installed capacity characteristic matrix to obtain an average wind and solar resource characteristic value for the study area. The present invention takes into account the information of the built wind and solar project sites, and through a weighted average method, can reflect the resource situation of the study area suitable for the development of wind power and photovoltaics to the greatest extent, implicitly taking into account the uneven distribution of wind and solar resources caused by factors such as terrain and topography, so that the obtained wind and solar resource characteristics are more representative, can better serve the work of new energy resource assessment and power generation forecasting, and improve the accuracy of new energy resource assessment and power generation forecasting.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind and solar resource processing, and specifically relates to a weighted processing method for average wind and solar resources in a research area. Background Art

[0002] Wind energy and solar energy are the most commonly used wind and solar resources for new energy power generation technology. Their cleanliness, commonness and common use are important reasons for the long-term development of wind and solar power generation technology. In the prior art, when statistically analyzing wind and solar resource data for a certain study area, the average wind and solar resource data of the study area is often used directly. However, due to the uneven distribution of wind and solar resources in the study area, the average wind and solar resource data obtained in this way cannot effectively represent the characteristics of wind and solar resources in the study area. When using this average wind and solar resource data for new energy power generation prediction, the accuracy of the prediction results is reduced. Summary of the invention

[0003] In view of the defects of the prior art, the present invention provides a weighted processing method for the average wind and solar resources in a study area, which can effectively solve the above problems.

[0004] The technical solution adopted by the present invention is as follows:

[0005] The present invention provides a weighted processing method for average wind and solar resources in a research area, comprising the following steps:

[0006] Step S1, obtaining a scatter plot of the wind and solar project sites and installed capacity distribution in the study area; in the scatter plot, there are m wind and solar project sites, and the original coordinates of any k-th wind and solar project site are (x 0k ,y 0k ), the installed capacity is p k ; where k = 1, 2, ..., m;

[0007] Step S2: the original coordinates (x 0k ,y 0k ) to obtain the representative adjusted site coordinates of the wind and solar resources (x k ,y k );

[0008] Step S3, using a clustering algorithm, clustering the adjusted wind and solar project site coordinates in the study area according to the principle of similar Euclidean distance, to obtain n wind and solar project site clustering areas; for any I-th wind and solar project site clustering area, I = 1, 2, ..., n, the center point position coordinates of the clustering area are (x I ,y I); add up the installed capacity of each wind and solar project site in the I-th wind and solar project site cluster area to obtain the total installed capacity p of the I-th wind and solar project site cluster area I ;

[0009] Step S4, the study area is divided into multiple grids according to the spatial resolution of the wind and solar resources; the corner points of each grid are grid points, and each grid point position has a corresponding wind and solar resource. Specifically, for the coordinates (X i ,Y i ) grid point, its scenery resource is R(X i ,Y i ), thus we get the wind and solar resource matrix R = {R(X i ,Y i )}, where i = 1, 2, ..., N, and N represents the number of grid points in the study area;

[0010] Step S5, distribute the total installed capacity of each wind and solar project site cluster area in the study area to the four grid points of the nearest grid, so as to obtain the installed capacity of each grid point in the study area. Specifically, for the grid point with coordinates (X i ,Y i ) grid point, whose installed capacity is P(X i ,Y i ), and then normalize it to get the normalized installed capacity P′(X i ,Y i ), i = 1, 2, ..., N, and thus the installed capacity characteristic matrix P = {P′(X i ,Y i )};

[0011] Step S6: Use the following formula to convert the wind and solar resource matrix R of the research area into i ,Y i )} and installed capacity characteristic matrix P = {P′(X i ,Y i )}Cross multiply and sum:

[0012]

[0013] Thus, the average wind and solar resource characteristic value r of the study area is obtained after weighting by the wind and solar project site location and installed capacity.

[0014] Preferably, step S2 specifically comprises:

[0015] Step S2.1, import the wind and solar project sites and installed capacity distribution scatter plots in the study area into the geographic information processing platform, and import the geographic elevation information DEM data;

[0016] Step S2.2, read the original coordinates (x 0k ,y 0k ) Elevation data of the location 0k ;

[0017] Step S2.3, with the original coordinates (x 0k ,y 0k ) position as the center, establish a buffer zone with a radius of l; search for the elevation data d at the highest point in the buffer zone max,k , calculate the elevation data d 0k and elevation data d max,k The difference Δd k =d max,k -d 0k ; If the difference Δd k If it is greater than the set value, the elevation data d max,k The center point coordinates (x maxk ,y maxk ), as the original coordinate (x 0k ,y 0k ) The adjusted site coordinates of the wind and solar project (x k ,y k ); otherwise, for the original coordinates (x 0k ,y 0k ) is not adjusted.

[0018] Preferably, in step S5, for each coordinate (X i ,Y i ) grid point, its installed capacity P(X i ,Y i ) is determined as follows:

[0019] For each grid, determine whether there is a central point of a certain clustering area inside it. If not, the installed capacity of the four grid points of the grid is 0; if it exists, the total installed capacity of the clustering area inside it is used as the installed capacity of the four grid points of the grid.

[0020] The weighted processing method for average wind and solar resources in the research area provided by the present invention has the following advantages:

[0021] The present invention takes into account the site information of existing wind and solar projects, and through weighted averaging, can reflect the resource situation of the study area suitable for wind power and photovoltaic development areas to the greatest extent, and implicitly takes into account the uneven distribution of wind and solar resources caused by factors such as terrain and topography. Therefore, the obtained wind and solar resource characteristics are more representative, which can better serve the work of new energy resource assessment and power generation forecasting, and improve the accuracy of new energy resource assessment and power generation forecasting. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of a flow chart of a weighted processing method for average wind and solar resources in a research area provided by the present invention;

[0023] Figure 2 A schematic diagram of the site coordinate adjustment principle of a wind and solar project provided by the present invention;

[0024] Figure 3 A schematic diagram of clustering the site coordinates of a wind and solar project provided by the present invention;

[0025] Figure 4 The total installed capacity of the wind and solar project site cluster area provided by the present invention is distributed to the four grid points of the nearest grid. DETAILED DESCRIPTION

[0026] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0027] The present invention provides a weighted processing method for average wind and solar resources in a study area. The method is based on the site information of built wind and solar projects in the study area and high-resolution wind and solar resource data. According to the location coordinates and range of the built wind and solar project sites in the study area, the wind and solar project site coordinates under the corresponding longitude and latitude conditions are clustered, and the wind and solar resources are weighted averaged with the total installed capacity of the clustering area as the weight to obtain the average wind and solar resource characteristic value of the study area, including physical quantities such as characteristic wind speed and cumulative irradiation value. Different from the traditional method, the present invention takes into account the site information of built wind and solar projects, and through weighted averaging, it can reflect the resource situation of the study area suitable for wind power and photovoltaic development areas to the greatest extent, and implicitly takes into account the uneven distribution of wind and solar resources caused by factors such as terrain and topography. Therefore, the obtained wind and solar resource characteristics are more representative, which can better serve the work of new energy resource assessment and power generation forecasting, and improve the accuracy of new energy resource assessment and power generation forecasting.

[0028] See also Figure 1 The present invention provides a weighted processing method for average wind and solar resources in a research area, comprising the following steps:

[0029] Step S1, obtaining a scatter plot of the wind and solar project sites and installed capacity distribution in the study area; in the scatter plot, there are m wind and solar project sites, and the original coordinates of any k-th wind and solar project site are (x 0k ,y 0k ), the installed capacity is p k ; where k = 1, 2, ..., m;

[0030] Step S2: the original coordinates (x 0k ,y 0k ) to obtain the representative adjusted site coordinates of the wind and solar resources (x k ,y k );

[0031] Step S2 is specifically as follows:

[0032] Step S2.1, import the wind and solar project sites and installed capacity distribution scatter plots in the study area into the geographic information processing platform, and import the geographic elevation information DEM data;

[0033] Step S2.2, read the original coordinates (x 0k ,y 0k ) Elevation data of the location 0k ;

[0034] Step S2.3, with the original coordinates (x 0k ,y 0k ) position as the center, establish a buffer zone with a radius of l; search for the elevation data d at the highest point in the buffer zone max,k , calculate the elevation data d 0k and elevation data d max,k The difference Δd k =d max,k -d 0k ; If the difference Δd k If it is greater than the set value, the elevation data d max,k The center point coordinates (x maxk ,y maxk ), as the original coordinate (x 0k ,y 0k ) The adjusted site coordinates of the wind and solar project (x k ,y k ); otherwise, for the original coordinates (x 0k ,y 0k ) is not adjusted.

[0035] Step S3, using a clustering algorithm, clustering the adjusted wind and solar project site coordinates in the study area according to the principle of similar Euclidean distance, to obtain n wind and solar project site clustering areas; for any I-th wind and solar project site clustering area, I = 1, 2, ..., n, the center point position coordinates of the clustering area are (x I ,y I); add up the installed capacity of each wind and solar project site in the I-th wind and solar project site cluster area to obtain the total installed capacity p of the I-th wind and solar project site cluster area I ;

[0036] Step S4, the study area is divided into multiple grids according to the spatial resolution of the wind and solar resources; the corner points of each grid are grid points, and each grid point position has a corresponding wind and solar resource. Specifically, for the coordinates (X i ,Y i ) grid point, its scenery resource is R(X i ,Y i ), thus we get the wind and solar resource matrix R = {R(X i ,Y i )}, where i = 1, 2, ..., N, and N represents the number of grid points in the study area;

[0037] Step S5, distribute the total installed capacity of each wind and solar project site cluster area in the study area to the four grid points of the nearest grid, so as to obtain the installed capacity of each grid point in the study area. Specifically, for the grid point with coordinates (X i ,Y i ) grid point, whose installed capacity is P(X i ,Y i ), and then normalize it to get the normalized installed capacity P′(X i ,Y i ), i = 1, 2, ..., N, and thus the installed capacity characteristic matrix P = {P′(X i ,Y i )};

[0038] In step S5, for each coordinate (X i ,Y i ) grid point, its installed capacity P(X i ,Y i ) is determined as follows:

[0039] For each grid, determine whether there is a central point of a certain clustering area inside it. If not, the installed capacity of the four grid points of the grid is 0; if it exists, the total installed capacity of the clustering area inside it is used as the installed capacity of the four grid points of the grid.

[0040] Step S6: Use the following formula to convert the wind and solar resource matrix R of the research area into i ,Y i )} and installed capacity characteristic matrix P = {P′(X i ,Y i )}Cross multiply and sum:

[0041]

[0042] Thus, the average wind and solar resource characteristic value r of the study area is obtained after weighting by the wind and solar project site location and installed capacity.

[0043] The present invention provides a weighted processing method for average wind and solar resources in a research area, which can be summarized as follows:

[0044] Step 1: Based on the site coordinates of wind power and photovoltaic projects in the study area, calibrate the site coordinates of wind and solar power projects so that they fall at a nearby location with a suitable elevation, and obtain a scatter plot of the site coordinate information of wind and solar power projects at a reasonable elevation.

[0045] Step 2: Using the K-means clustering algorithm, the wind and solar project sites in the study area are clustered into n categories based on the principle of close distance. Considering the number of wind and solar project sites in the study area, n can be 3 or 4.

[0046] Step 3: Obtain the clustered wind and solar project site clustering area through clustering algorithm, calculate the coordinates of the center point of the wind and solar project site clustering area, and summarize the total installed capacity of the clustering area.

[0047] Step 4: Extract the center point coordinates and total installed capacity of the wind and solar project site cluster area, match them to the grid points with the same resolution as the wind and solar resources, obtain the wind and solar project site cluster area feature matrix, normalize the feature matrix, and obtain the normalized wind and solar project site cluster area feature matrix.

[0048] Step 5: By cross-multiplying and summing the normalized wind and solar project site cluster area characteristic matrix and the wind and solar resource matrix, the weighted average wind and solar resource characteristic value of the study area can be obtained.

[0049] A specific embodiment is described below:

[0050] Step 1: Sort out the coordinates of hundreds of wind power and photovoltaic project sites in the study area, and convert the original coordinates (x 0k ,y 0k ) is imported into the ARCGIS geographic information processing platform, and the national 90-meter resolution geographic elevation information DEM data is imported.

[0051] Build a coordinate processing toolbox based on the PYTHON programming language to read the original coordinates (x 0k ,y 0k ) Elevation data of the point 0k ,like Figure 2As shown in the figure, a buffer zone with a radius of 1 km is established near the coordinate point, and the elevation data d at the highest point in the buffer zone is searched. max,k , compare and calculate the difference Δd between the coordinate elevation of the center point of each site and the highest point elevation in the vicinity k =d max,k -d 0k , determine when Δd k >200, the center coordinates of the highest elevation position (x maxk ,y maxk ) to replace the original center point coordinates, so that the elevation of the wind farm site meets the higher elevation level of the region, and its wind speed resources are representative. Figure 2 In the figure, point A1 is the original coordinate (x 0k ,y 0k ) and A2 is the point after replacement and adjustment. The coordinate information of the wind power and photovoltaic project sites before and after replacement is sorted out to obtain the scatter plot of wind power and photovoltaic site coordinate information at reasonable elevation (x k ,y k ).

[0052] Step 2: Based on the coordinates of the wind power and photovoltaic project sites at reasonable elevations (x k ,y k ), use the K-means clustering algorithm, perform clustering according to the principle of similar Euclidean distance, input the location coordinates of all wind power and photovoltaic sites in the study area, call the K-means clustering toolbox in MATLAB, input the number of cluster types n, and cluster the wind power and photovoltaic projects in the study area according to their geographical locations. Considering the number and distribution characteristics of wind power and photovoltaic sites in the study area, the cluster type data n can be taken as 3 or 4. The calling code is: [idregion,c]=kmeans(location,n), where idregion is the classification indicator number of the clustering result, which is less than or equal to the number of types n, c is the center point location information of each cluster type, and location is the location coordinate vector of each wind power and photovoltaic site. Figure 3 As shown, this is a schematic diagram of clustering the site coordinates of a wind and solar project.

[0053] Step 3: Based on the K-means clustering algorithm, the clustered wind power and photovoltaic project clusters are obtained, and the coordinates of the center point of the cluster (x I ,y I ), the installed capacity of all wind power and photovoltaic projects in the cluster area is p i Add together to get the total installed capacity p of the cluster area I ,in

[0054] Step 4: Based on the extracted center point coordinates (x I ,y I ) and installed capacity p I First, we construct a characteristic matrix of wind and solar project clusters with the same spatial resolution as wind and solar resources. By searching the coordinate position of each grid, we match the coordinates of the center points of the wind and photovoltaic project clusters to the nearest grid, and assign the total installed capacity of the corresponding cluster to the four grid points, such as Figure 4 As shown, the calculation formula is:

[0055]

[0056] Where P(X i ,Y i ) is the coordinate (X i ,Y i ) is the characteristic matrix of the wind and solar project cluster area at the location. The grid value of the grid point where there is a wind power and photovoltaic cluster area is the corresponding installed capacity, and the grid value of the grid point where there is no wind power and photovoltaic cluster area is 0. The characteristic matrix is ​​further normalized, and the calculation formula is: P′(X i ,Y i )=P(X i ,Y i ) / ∑P(X i ,Y i ), and obtain the normalized characteristic matrix of the wind and solar project cluster area.

[0057] Step 5: Normalize the characteristic matrix of the wind and solar project cluster area P = {P′(X i ,Y i )} and the wind and solar resource matrix R = {R(X i ,Y i )} Cross-multiply and sum, the formula is:

[0058]

[0059] The average wind and solar resource characteristic value of the study area can be obtained after weighting by the wind and solar site location and installed capacity.

[0060] In the prior art, the provincial wind and solar resource averaging method is relatively rough, and does not take into account the uneven distribution of wind and solar resources in the study area caused by terrain, topography, etc., nor does it take into account the influence of the preference and centralized layout of wind and solar power stations in site selection on the characteristic values ​​of wind and solar resources. The present invention is based on the site information of built wind power and photovoltaic projects across the country and the national high-resolution wind and solar resource meteorological data. According to the location coordinates and range of built wind power and photovoltaic sites within the regional divisions of each province, the site coordinates of wind and solar projects under the corresponding longitude and latitude conditions are clustered, and the installed capacity is used as the weight. The weighted average can obtain physical quantities such as characteristic wind speed and cumulative irradiation value at the provincial scale. Different from other methods, the present invention takes into account the site information of built wind and solar power stations. Through weighted averaging, it can reflect the resource situation of the study area suitable for the development of wind power and photovoltaics to the greatest extent, implicitly taking into account the uneven distribution of wind and solar resources caused by factors such as terrain and topography, and can better serve the provincial new energy resource assessment and power generation forecasting.

[0061] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be considered as the scope of protection of the present invention.

Claims

1. A weighted processing method for average wind and solar resources in a research area, characterized in that: The following steps are involved: Step S1, obtaining a scatter plot of wind and solar project sites and installed capacity distribution in the study area; In the scatter plot, there are m wind and solar project sites, and the original coordinates of any k-th wind and solar project site are (x 0k ,y 0k ), the installed capacity is p k ; where k = 1, 2, ..., m; Step S2: the original coordinates (x 0k ,y 0k ) to obtain the representative adjusted site coordinates of the wind and solar resources (x k ,y k ); Step S3, using a clustering algorithm, clustering the adjusted wind and solar project site coordinates in the study area according to the principle of similar Euclidean distance, to obtain n wind and solar project site clustering areas; for any I-th wind and solar project site clustering area, I = 1, 2, ..., n, the center point position coordinates of the clustering area are (x I ,y I ); add up the installed capacity of each wind and solar project site in the I-th wind and solar project site cluster area to obtain the total installed capacity p of the I-th wind and solar project site cluster area I ; Step S4, the study area is divided into multiple grids according to the spatial resolution of the wind and solar resources; the corner points of each grid are grid points, and each grid point position has a corresponding wind and solar resource. Specifically, for the coordinates (X i ,Y i ) grid point, its scenery resource is R(X i ,Y i ), thus we get the wind and solar resource matrix R = {R(X i ,Y i )}, where i = 1, 2, ..., N, and N represents the number of grid points in the study area; Step S5, distribute the total installed capacity of each wind and solar project site cluster area in the study area to the four grid points of the nearest grid, so as to obtain the installed capacity of each grid point in the study area. Specifically, for the grid point with coordinates (X i ,Y i ) grid point, whose installed capacity is P(X i ,Y i ), and then normalize it to get the normalized installed capacity P′(X i ,Y i ), i = 1, 2, ..., N, and thus the installed capacity characteristic matrix P = {P′(X i ,Y i )}; Step S6: Use the following formula to convert the wind and solar resource matrix R of the research area into i ,Y i )} and installed capacity characteristic matrix P = {P′(X i ,Y i )}Cross multiply and sum: Thus, the average wind and solar resource characteristic value r of the study area is obtained after weighting by the wind and solar project site location and installed capacity.

2. According to claim 1, a weighted processing method for average wind and solar resources in a research area is characterized in that: Step S2 is specifically as follows: Step S2.1, import the wind and solar project sites and installed capacity distribution scatter plots in the study area into the geographic information processing platform, and import the geographic elevation information DEM data; Step S2.2, read the original coordinates (x 0k ,y 0k ) Elevation data of the location 0k ; Step S2.3, with the original coordinates (x 0k ,y 0k ) position as the center, establish a buffer zone with a radius of l; search for the elevation data d at the highest point in the buffer zone max,k , calculate the elevation data d 0k and elevation data d max,k The difference Δd k =d max,k -d 0k ; If the difference Δd k If it is greater than the set value, the elevation data d max,k The center point coordinates (x maxk ,y maxk ), as the original coordinate (x 0k ,y 0k ) The adjusted site coordinates of the wind and solar project (x k ,y k ); otherwise, for the original coordinates (x 0k ,y 0k ) is not adjusted.

3. The weighted processing method of average wind and solar resources in a research area according to claim 1 is characterized in that: In step S5, for each coordinate (X i ,Y i ) grid point, its installed capacity P(X i ,Y i ) is determined as follows: For each grid, determine whether there is a central point of a certain clustering area inside it. If not, the installed capacity of the four grid points of the grid is 0; if it exists, the total installed capacity of the clustering area inside it is used as the installed capacity of the four grid points of the grid.

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